Length of Study: 2 years
Min. Credits: 120
Degree Programme: Information Technology and Artificial Intelligence
Language of Instruction: Czech
Form of Study: full-time
Accredited from: 2019 Accredited till: 2029
The goal of the study is to teach students knowledge and skills in the field of computer vision and related subjects. The tought knowledge includes the geometry of cameras, image formation and necessary basics of machine learning and artificial intelligence. The graduates will be ready to develop systems of computer vision and apply them in the practice.
Obtaining knowledge about image formation in a camera, about image processing and machine learning used in computer vision. Obtaining practical experience with development of image processing and computer vision systems.
The achieved knowledge includes:
- knowledge of image processing, usage of software libraries for image processing, development of image processing algorithms,
- knowledge of basic tasks of computer vision: detection, classification, geometric scene understanding, segmentation, etc.
- practical experience with development of computer vision systems for solving particular tasks.
State Exam in Information Technology and Artificial Intelligence, specialization Computer Vision consists of the following parts:
- presentation and defense of master's thesis,
- oral exam, which combines the basic themes contained in the courses profiling the basis of Information Technology and Artificial Intelligence (Theoretical Computer Science, Statistics and Probability, Computer Systems Architectures, Artificial Intelligence and Machine Learning, Data Storage and Preparation, Functional and Logic Programming, Parallel and distributed algorithms, Modern trends in informatics),
- oral exam, which combines the basic themes contained in the courses profiling the basis of Computer Vision (Computer Graphics, Computational Geometry, Computer Vision, Image Processing, Graphical and Sound Interfaces and Standards, Convolutionary Neural Networks).
All parts of the state examination are held on the same date before the State Examination Board. The state exam can be taken by a student who has obtained the required number of credits in the prescribed composition necessary for the successful completion of the master's degree and has submitted the master's thesis in due time. The organization and course of the state examination are given by the corresponding internal standard of the faculty and by the relevant instructions of the program guarantor for state examinations.
- Reconstruction of 3D information about automobiles observed by a surveillance camera
- Optical localization of very distant targets in a multi-camera system
- Counting pedestrians in video
- Deep neural networks for face recognition in video
- Recognition of historical texts by using deep neural networks
- Deblurring of X-ray images with geometric blur
- Estimation of personality characteristics from video
Choose academic year and curriculum
|AVS||Computation Systems Architectures||5||C||Cr+Ex||FIT|
|MSP||Statistics and Probability||6||C||Cr+Ex||FME|
|SUI||Artificial Intelligence and Machine Learning||5||C||Ex||FIT|
|TIN||Theoretical Computer Science||7||C||Cr+Ex||FIT|
|UPA||Data Storage and Preparation||5||C||Cr+Ex||FIT|
|FLP||Functional and Logic Programming||5||C||Cr+Ex||FIT|
|PRL||Parallel and Distributed Algorithms||5||C||Cr+Ex||FIT|
|PP2||Project Practice 2||5||E||ClCr||FIT|
Duty: C - compulsory, CEx - compulsory-elective group x, R - recommended, E - elective